Visual storytelling with archetypes: howsingle-image content can tell cohesive and consistent brand stories
Bibliographic record
Abstract
Purpose Despite the rise of visual content in marketing practice, guidance on visual storytelling remains scarce. Integrating neo-archetypal theory with practice theory, this paper examines whether archetypes can facilitate the translation of specific plot-based brand stories into still images, and how managers can select single-image content to create cohesive and consistent articulations of archetypes over time. Design/methodology/approach This paper examines the use of archetypes in visual storytelling at both outcome (i.e. how well brands enact archetypes) and process-levels (i.e. what managerial actions and thought patterns drive variations in archetype consistency): The study performs a critical visual analysis of 1,244 Instagram posts across seven wine brands to identify dominant character archetypes expressed through these images. Furthermore, the study conducts 10 depth interviews with representatives of the seven brands and professional storytellers to examine how managers select images to tell brand stories. Findings Findings demonstrate that archetypes can align meanings between plot-based brand stories and visual content. The study finds that some brands are more consistent than others in their archetypal enactments, and details how these differences are produced by competing teleoaffective structures that either promote or inhibit alignments of archetypal meanings. Originality/value The study findings contribute to the literatures on visual storytelling and archetypal marketing by advancing understandings of (a) the role of archetypes in visual storytelling (i.e. archetypes can express differentiated and nuanced brand meanings, but they must be enrolled in storytelling practices to affect how marketers act and what makes sense for them to do) and (b) how a visual mix is articulated in marketing practice (i.e. how competing goals can be balanced and what dangers are posed by overly emphasizing customer engagement metrics).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".